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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

SN computer Science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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The classification of medical and botanical data through majority voting using artificial neural network

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3271-3283页

作者： Tripathi, Kshitij Khan, Fayaz Ahmed Khanday, Akib Mohi Ud Din Nisa, Khair U. L. Department of Computer Applications The Maharaja SayajiRao University of Baroda Vadodara 390002 India Department of Information Technology S.P. College Cluster University Srinagar 190001 India Department of Computer Science and Software Engineering-CIT United Arab Emirates University Al Ain United Arab Emirates

Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that might belong to various domains like chemical, botanical, medical, spatial, textual, and image. In this work, an ANN technique is applied to the 7 Life sciences (botanical and medical) data sets extracted from public data repositories. Various optimization approaches like exhaustive validation, cross-validation, and multiple seeding are used to discover the most optimized networks for the given datasets. Finally, voting predicts the class where the whole dataset is used as a test set instead of folds. The results obtained by the proposed approach outperform other approaches on all the datasets. Cleveland’s heart, Statlog heart, Dermatology, Hepatitis, Seeds, Abalone and Vertebral Column data sets (all of UCI) after applying the voting showed the accuracy of 94.61%, 93.7%, 99.73%, 96.77%, 99.05%, 89.37% and 90.32% respectively. In the future deep neural network may be used to improve the results. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Artificial neural network Classification Optimization Validation

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Two-branch Convolutional Networks for Stress Detection from Biomedical Data

Two-branch Convolutional Networks for Stress Detection from ...

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International Conference on Soft Computing and Measurements (SCM)

作者： Maksim O. Dobrokhvalov Anton Yu. Filatov Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Stress is considered one of the main causes of health problems. Combining physiological signals from multiple modalities is a promising method for more accurately determining a person’s condition. This paper proposes single-branch and two-branch convolutional neural networks for stress detection using biomedical data (electrocardiogram, blood pulse pressure, electrodermal activity). The two-branch model independently processes heart data (electrocardiogram or blood volume pulse) and electrodermal activity, and then merges the results of the branches using a predictor. The work examines various predictors. In some predictors, only the final result of the network is transferred to the loss function, and in one, the results of each branch and the whole network are taken into account. On the WESAD dataset, the single-branch model achieves an accuracy 0.983 and an F1 score 0.976, and the two-branch model achieves an accuracy 0.994 and an F1 score 0.992. On the UBFC-Phys dataset, the single-branch model achieves an accuracy of 0.922 and an F1 score of 0.945.

关键词： Accuracy Biological system modeling Volume measurement computer architecture Physiology Convolutional neural networks Bioinformatics

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An Intelligent Method for Detecting Jute Pests Using Deep Convolutional Neural Networks 5

An Intelligent Method for Detecting Jute Pests Using Deep Co...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Vignesh, S. Prabhu, S. Sampathkumar, R. Rani, M. Thillai Rejina Parvin, J. Maheswari, G. Uma Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Mechatronics Engineering Tamil Nadu Coimbatore India Sri Krishna College of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Pest attacks pose a serious threat to the production of jute and other significant crops. Jute farmers often utilize their visual sense and hands-on expertise to distinguish between multiple diseases that seem to be identical. The intelligent model we built for the identification of jute pests was based on a deep convolutional neural network (DCNN). This practical difficulty led to the development of this concept. The proposed DCNN model can automatically identify jute pest concerns with high speed and accuracy based on image recognition. In addition, images of the four most common jute bugs are included in an organized image graphics collection. Our model yields a DCNN accuracy is 96.72% for the four most significant jute pest kinds. Further proof of the model's effectiveness is provided by the accuracy, recall, F1-score, and confusion matrix metrics. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Transfer Learning Performance for Abnormality Detection in Brain MRI Images Using Feature Optimization Techniques

Improving Transfer Learning Performance for Abnormality Dete...

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International Conference on Soft Computing and Measurements (SCM)

作者： Yasser A. Nizamli Anton Yu. Filatov Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Accurate and early diagnosis of brain tumors plays a vital role in determining appropriate treatment and increasing survival rates. Manual diagnosis using MRI images is often prone to errors and can be a tedious and time-consuming procedure. As a result, automated computer-aided systems that utilize artificial intelligence and machine learning techniques are widely used. However, accurately diagnosing the type of brain tumor remains a significant and complex challenge for today’s medical imaging research community. In this paper, we present an enhanced transfer learning approach that aims to differentiate brain tumor types using MRI scans while achieving state-of-the-art performance. In the proposed approach, the pre-trained VGG-19 network with fixed weights is used to map MRI scans to a high-level numerical representation. The extracted features are then passed to a support vector machine algorithm to classify brain tumors into one of three major categories: glioma, meningioma, and pituitary. To further improve the performance of the model, the effectiveness of utilizing feature optimization techniques based on the radial basis similarity function and L1-penalty logistic regression was investigated. For evaluation, the benchmark Figshare dataset containing 3064 MRI images was used after a series of processing operations. The proposed system achieved a high overall accuracy of 98.53%, outperforming other analogues.

关键词： Logistic regression Accuracy Magnetic resonance imaging Computational modeling Transfer learning Feature extraction Brain modeling

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A Machine Learning Approach for Predicting Air Quality Index in Smart Cities 5

A Machine Learning Approach for Predicting Air Quality Index...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Swamynathan, S. Sneha, N. Ramesh, S.P. Niranjana, R. Ponkumar, D. David Neels Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science & Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

One essential natural resource, the air quality index has been deteriorated by economic activities. Many studies have been conducted about the forecasting of periods of unsafe atmospheric conditions;however, the majority of these studies are limited in their capacity to include seasonal and other factors due to insufficient longitudinal data. India noise pollution has compiled a 6-year dataset that has been used to build several prediction models. There are encouraging outcomes when AQI levels are predicted using machine learning methodologies such as support vector machines (SVMs) artificial neural networks (ANNs), and Naive Bayes (NB). However, in several tests involving datasets from three different locations, NB, ANN, and SVM performed well to maximize prediction performance. When it comes to mean absolute error (MAE), R-squared (R2), and Root-mean-square deviation (RMSE), the combined performance of stacks is unsurpassed. In the related work section, summarize the limitations of existing models, such as inadequate data preprocessing, low spatialtemporal resolution, limited feature selection, or overfitting in complex models. Highlight gaps in scalability, real-time adaptability, and generalization across regions, emphasizing the need for robust, efficient models tailored to air quality prediction. © 2024 IEEE.

关键词： Support vector machines

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Implementation of multicloud strategies for healthcare organisations to avoid cloud sprawl

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International Journal of Cloud Computing 2022年第5-6期11卷 529-536页

作者： Jayanthiladevi, A. Ayoobkhan, Mohamed Uvaze Ahamed ThamaraiSelvi, R. Jimmy, Laishram Mishra, Piyush Robert, Nismon Rio Industry Relations and International Research Collaborations Srinivas University Karnataka India Software Engineering New Uzbekistan University Tashkent Uzbekistan Department of MCA Bishop Heber College Trichy India Department of Computer Science and Engineering Manipur Technical University Government Polytechnic Campus Manipur Imphal India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India Department of Computer Science Christ University Bengaluru India

Healthcare organisations are being overwhelmed by data, devices and apps, and disjointed multiple cloud services. Well-heeled multicloud can provide a unified cloud model that provides greater control and scalability at reduced costs. Healthcare multicloud is turning into an appealing path for associations to manage the blast of advanced healthcare information in digital health, internet of things, associated gadgets and healthcare applications. As more human services associations grasp distributed computing, they are progressively going to a blend of open, private, and hybrid cloud administrations and foundation. In fact most of the healthcare service organisations plan to utilise different cloud vendors over the upcoming years. While having multiple cloud vendors can give organisations more flexibility and redundancy, managing multiple clouds can be a challenge. It can lead to cloud sprawl, unauthorised cloud use known as shadow IT, disjointed cloud solutions, inefficiencies, and waste. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Health care

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Intelligent Task Offloading and Collaborative Computation over D2D Communication

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China Communications 2021年第3期18卷 251-263页

作者： Cuili Jiang Tengfei Cao Jianfeng Guan Department of Computer Technology and Applications Qinghai UniversityXining 810016China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and *** the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation ***,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high ***,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,***,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

关键词： utility maximization lyapunov optimization task offloading mobile edge computing

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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

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2024 International Conference on Decision Aid Sciences and applications, DASA 2024

作者： Qader Ismail Alnajem, Ahmed Abdel Ibrahim, Amer Abdulrahman, Mayasa M. Abdulrazak Al-Kubaisi, Abdulrazak A. Tawfeq, Jamal Fadhil Khishe, Mohammad Saeed, Ali Baghdad College of Economic Sciences Business Administration Department Iraq College of Information Technology Department of Computer Science and Software Engineering United Arab Emirates College of Engineering Computer Engineering Department University of Baghdad Baghdad Iraq Medical Technical College Department of Medical Instrumentation Technical Engineering Al-Farahidi University Baghdad00965 Iraq Yuan Ze University Innovation Center for Artificial Intelligence Applications Taiwan Jadara University Jadara Research Center Jordan Northern Technical University Computer Center Ninevah Iraq

ISBN: (纸本)9798350369106

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage. © 2024 IEEE.

关键词： Competition

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